SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 982 papers

TitleStatusHype
A Survey on Malware Detection with Graph Representation Learning—0
Topological Pooling on GraphsCode0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail—0
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Creating generalizable downstream graph models with random projections—0
Learnable Topological Features for Phylogenetic Inference via Graph Neural NetworksCode1
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Is Distance Matrix Enough for Geometric Deep Learning?Code1
A Survey on Spectral Graph Neural Networks—0
Heterophily-Aware Graph Attention Network—0
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal KrigingCode1
Spectral Augmentations for Graph Contrastive Learning—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Simultaneous Linear Multi-view Attributed Graph Representation Learning and ClusteringCode1
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption—0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Logical Message Passing Networks with One-hop Inference on Atomic FormulasCode1
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Everything is Connected: Graph Neural Networks—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning—0
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation LearningCode0
A Generalization of ViT/MLP-Mixer to GraphsCode1
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Graph Learning with Localized Neighborhood Fairness—0
Data Augmentation on Graphs: A Technical SurveyCode1
Robust Graph Representation Learning via Predictive Coding—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Learning Graph Search Heuristics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified